class_docstr: Text Classification Model.
  
  The model takes word embeddings, puts them into an RNN to obtain a text
  representation, and puts the text representation in the end into a linear
  layer to get the actual class label. The model can handle single and multi
  class data sets.
class_docstr_tok: ["Text","Classification","Model",".","The","model","takes","word","embeddings",",","puts","them","into","an","RNN","to","obtain","a","text","representation",",","and","puts","the","text","representation","in","the","end","into","a","linear","layer","to","get","the","actual","class","label",".","The","model","can","handle","single","and","multi","class","data","sets","."]
code: def _get_data_points_from_sentence(
      self, sentence: Sentence
  ) -> List[Sentence]:
      return [sentence]
  
file_path: flair/flair/models/text_classification_model.py
imports: import logging
  from pathlib import Path
  from typing import Any, Dict, List, Union
  import torch
  import flair.embeddings
  import flair.nn
  from flair.data import Sentence
  from flair.file_utils import cached_path
label_desc: Use image node_id 3 for calling the TextClassifier obj's underlying member method code with example usage: obj._get_data_points_from_sentence(sentence) and returns: list